Configurations.
Our TranSVAE is implemented with PyTorch. We use Adam with a weight decay of \(1e^{-4}\)  as the optimizer. The learning rate is initially set to be \(1e^{-3}\)  and follows a commonly used decreasing strategy in [1]}.
The batch size and the learning epoch is uniformly set to be 128 and 1,000, respectively, for all the experiments.
We uniformly set 100 epochs of training under only source supervisions and involve the target pseudo-labels afterward.
Following the common protocol in video-based UDA [2]}, we perform hyperparameter selection on the validation set. NVIDIA A100 GPUs are used for all experiments. Kindly refer to our Appendix for all other details.
